Hi Phil, ----- Mail original ----- > In my own applications, I noticed what appears to be poor > performance in the nextInt(int) method of the Mersenne twister, > which I was using to *improve* speed. I think that for small n, the > default implementation in BistreamGenerator may be running too many > iterations.
Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. > I am still figuring out how the code works, but I > thought it would be good to run some benchmarks - using Gilles' new > stuff - against the Harmony implementation in java.util.Random of > this method. That led me to notice that there are no unit tests for > BitstreamGenerator. I propose that we add > 0) RandomGeneratorAbstractTest with an abstract makeGenerator > method including fixed seed tests for all RandomGenerator methods > 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest > implementing makeGenerator with a BitStreamGenerator that uses the > JDK generator for next(int) > 2) Make the test classes for Mersenne and Weil generators extend > RandomGeneratorAbstractTest, moving redundant tests up into the base > class > > Sound reasonable? +1 > Also, any recollection why we are using a > different implementation in BitStreamGenerator for next(int) than > Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. Mersenne twister and Well should be fast for generating large sets, but most importantly they have very good and *proven* properties (equidistribution on large dimensions, null correlation, maximal period ...). These properties are essential for example in Monte-Carlo simulations with lots of variables that must be independent or have controlled correlations. Luc > The Harmony impl is almost identical to > what is documented in the JDK javadoc. > > Phil > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
